Abstract
This paper introduces a novel local model for the classification of covariance matrices: the co-occurrence matrix of covariance matrices. Contrary to state-of-the-art models (BoRW, R-VLAD and RFV), this local model exploits the spatial distribution of the patches. Starting from the generative mixture model of Riemannian Gaussian distributions, we introduce this local model. An experiment on texture image classification is then conducted on the VisTex and Outex_TC000_13 databases to evaluate its potential.
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References
Liu, C., Sharan, L., Adelson, E.H., Rosenholtz, R.: Exploring features in a Bayesian framework for material recognition. In: CVPR, pp. 239–246. IEEE Computer Society (2010)
Hiremath, P., Pujari, J.: Content based image retrieval using color, texture and shape features. In: 2012 18th International Conference on Advanced Computing and Communications (ADCOM), pp. 780–784 (2007)
de Luis-García, R., Westin, C.F., Alberola-López, C.: Gaussian mixtures on tensor fields for segmentation: applications to medical imaging. Comput. Med. Imaging Graph. 35(1), 16–30 (2011)
Cirujeda, P., Cid, Y.D., Müller, H., Rubin, D.L., Aguilera, T.A., Loo, B.W., Diehn, M., Binefa, X., Depeursinge, A.: A 3-D Riesz-covariance texture model for prediction of nodule recurrence in Lung CT. IEEE Trans. Med. Imaging 35(12), 2620–2630 (2016)
Zhu, C., Yang, X.: Study of remote sensing image texture analysis and classification using wavelet. Int. J. Remote Sens. 19(16), 3197–3203 (1998)
Regniers, O., Bombrun, L., Lafon, V., Germain, C.: Supervised classification of very high resolution optical images using wavelet-based textural features. IEEE Trans. Geosci. Remote Sens. 54(6), 3722–3735 (2016)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007). doi:10.1007/978-3-540-75690-3_13
Vu, N.S., Dee, H.M., Caplier, A.: Face recognition using the POEM descriptor. Pattern Recogn. 45(7), 2478–2488 (2012)
Nguyen, T.P., Vu, N., Manzanera, A.: Statistical binary patterns for rotational invariant texture classification. Neurocomputing 173, 1565–1577 (2016)
Tuzel, O., Porikli, F., Meer, P.: Region covariance: a fast descriptor for detection and classification. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 589–600. Springer, Heidelberg (2006). doi:10.1007/11744047_45
Jayasumana, S., Hartley, R.I., Salzmann, M., Li, H., Harandi, M.T.: Kernel methods on the Riemannian manifold of symmetric positive definite matrices. In: IEEE CVPR, pp. 73–80 (2013)
Faraki, M., Harandi, M.T., Wiliem, A., Lovell, B.C.: Fisher tensors for classifying human epithelial cells. Pattern Recogn. 47(7), 2348–2359 (2014)
Faraki, M., Harandi, M.T., Porikli, F.: More about VLAD: a leap from Euclidean to Riemannian manifolds. In: IEEE CVPR, pp. 4951–4960, June 2015
Ilea, I., Bombrun, L., Germain, C., Terebes, R., Borda, M., Berthoumieu, Y.: Texture image classification with Riemannian Fisher vectors. In: IEEE ICIP, pp. 3543–3547 (2016)
Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15561-1_11
Perronnin, F., Liu, Y., Sánchez, J., Poirier, H.: Large-scale image retrieval with compressed Fisher vectors. In: The Twenty-Third IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, pp. 3384–3391 (2010)
Said, S., Bombrun, L., Berthoumieu, Y., Manton, J.H.: Riemannian Gaussian distributions on the space of symmetric positive definite matrices. IEEE Trans. Inf. Theory 63(4), 2153–2170 (2017)
Said, S., Bombrun, L., Berthoumieu, Y.: Texture classification using Rao’s distance on the space of covariance matrices. In: Geometric Science of Information (2015)
Faraki, M., Palhang, M., Sanderson, C.: Log-Euclidean bag of words for human action recognition. IET Comput. Vision 9(3), 331–339 (2015)
Higham, N.J.: Functions of Matrices: Theory and Computation. Society for Industrial and Applied Mathematics, Philadelphia (2008)
Vision Texture Database. MIT Vision and Modeling Group. http://vismod.media.mit.edu/pub/VisTex
Outex Texture Database. Center for Machine Vision Research of the University of Oulu. http://www.outex.oulu.fi/index.php?page=classification
Ledoux, A., Losson, O., Macaire, L.: Texture classification with fuzzy color co-occurrence matrices. In: IEEE ICIP, pp. 1429–1433, September 2015
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Ilea, I., Bombrun, L., Said, S., Berthoumieu, Y. (2017). Co-occurrence Matrix of Covariance Matrices: A Novel Coding Model for the Classification of Texture Images. In: Nielsen, F., Barbaresco, F. (eds) Geometric Science of Information. GSI 2017. Lecture Notes in Computer Science(), vol 10589. Springer, Cham. https://doi.org/10.1007/978-3-319-68445-1_85
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DOI: https://doi.org/10.1007/978-3-319-68445-1_85
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